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Data Engineering Tools: Everything you need to excel

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Data Engineer: everything you need to know about the job

While data is considered one of the most valuable corporate resources, its massive volume and diversity of format very often make it difficult to exploit. This is precisely why data engineers are emerging.But what are the best Data Engineering Tools?

Acting as a true Big Data architect, the data engineer manages an organization’s entire data infrastructure. Find out more about their role, missions, skills, tools and salary. Not forgetting the training required to become a data engineer.

What is a Data Engineer?

The Data Engineer is responsible for the company’s entire data infrastructure. In concrete terms, he/she prepares the data to make it suitable for analysis and decision-making. The data engineer intervenes at the beginning of the data process, collecting raw data from a multitude of sources. They then integrate it into a data warehouse or data lake. Having designed the organization’s database, the data manager must manage it efficiently to facilitate data exploitation. To this end, he or she automates all data processing tasks, from extraction to storage and cleansing, right through to data transformation.

Only then is the data ready for analysis by other experts (data analysts and data scientists).

Ultimately, the data engineer’s role is that of a facilitator.

What does a Data Engineer do and hat are the top Data Engineering Tools?

Since the ultimate aim of the data engineer is to provide data analysts and data scientists with information that is ready to use, thanks to the best Data Engineering Tools, he or she carries out a considerable amount of data preparation work. To this end, they must perform a number of tasks:

Collecting and storing data: as data sources are highly varied (social networks, field feedback, website, application, IoT, etc.), they need to find solutions for collecting them easily, notably via APIs. Once the data has been collected, it needs to be integrated into a centralized storage facility accessible to all.
Understanding user needs: to design a data infrastructure that meets the organization’s expectations, the data engineer must first identify its needs.

For example, by answering the following questions: What data is relevant? What is the best format? Where is the best place to store it? etc.

  • Guarantee access to data: with ready-to-use data. To achieve this, the data engineer must ensure data quality. This means cleansing duplicate, obsolete, false or erroneous data. They must also standardize data formats so that they can be easily read by the organization’s various tools.
  • Implement processes, tools and algorithms: as preparation work is particularly time-consuming, he or she must develop automated solutions for collecting, storing, preparing, modeling and updating data in real time.
  • Ensure compliance with regulations such as GDPR.
  • He/she must then ensure the anonymization of personally identifiable data, manage the data lifecycle, etc.

Depending on the company, the data engineer may carry out only some or all of the above tasks.

Despite its many strengths, Python is not suitable for all tasks. It is a “high-level” language. It is therefore not suitable for system-level programming.

Nor is it ideal for situations requiring independent cross-platform binaries. A stand-alone application for Windows, macOS and Linux will not be easy to code in Python.

Finally, Python is best avoided for situations where speed is an absolute priority for the application. Better to turn to C and C++ or other languages of the same ilk.

Every function and module is treated as an object by Python. This simplifies the writing of high-level code, but reduces speed.

The dynamism and malleability of objects make optimization difficult, even after compilation. As a result, Python is considerably slower than C/C++ or Java. However, mathematical and statistical operations can be accelerated using libraries such as NumPy and Pandas.

Python also uses a lot of white space. This is sometimes seen as an advantage, but also as a disadvantage. Some people dismiss the language because of this point, but it actually makes the syntax more readable.

What skills does a Data Engineer need, beside the Data Engineering Tools?

As the person in charge of the data infrastructure, the data engineer must first be able to set it up. To do this, he or she needs a range of technical skills:

  • Mastery of programming languages: both generalist and more specialized depending on the environment in which they work.
  • Mastery of different web environments: such as Hadoop, Hive or Spark.
  • Knowledge of major mathematical principles: to manipulate and transform data.
  • Data modeling: for table design.
  • Artificial intelligence: such as Machine Learning and Deep Learning. Advanced knowledge is not required. But since his job is to facilitate that of the data scientists, he must understand the key concepts of data science.

In addition to these hard skills, he or she must also possess a number of indispensable personal qualities, such as an ability to adapt to new technologies and a flair for communication.

What are the Data Engineering Tools I need to learn?

As an engineer, the data engineer needs to master a number of highly technical Data Engineering Tools. Here are the main ones:

  • Programming languages, such as Python, Java, Scala, C++… ;
  • SQL or noSQL data languages;
  • Database management systems;
  • ETL (Extract, Transform, Load) tools;
  • DevOps tools (version management, virtualization, APIs, monitoring, automation, etc.);
  • Storage technologies such as Cassandra or Neo4J:
  • Analytics solutions, such as Hbase and Hive.
  • Cloud Computing tools, such as AWS,
  • Google Cloud, Microsoft Azure, etc.

What are the differences between Data Scientist and Data Engineer?

Data engineers intervene at the beginning of the data process, while data scientists come in at the end. Thanks to their in-depth knowledge of machine learning and deep learning, data scientists are able to perform advanced predictive analyses and respond to specific organizational problems.

But to carry out effective analysis work, data scientists need large quantities of qualitative data. It is precisely for this reason that the role of data engineers is indispensable.

What are the differences between Data Analyst and Data Engineer?

Data analysts analyze data to help organizations achieve their objectives through more informed decision-making. He or she will exploit all the data made available by the data engineer in the data pipeline.

Thanks to simplified access to relevant information, he or she is able to produce dashboards, reports and data visualizations, enabling better decision-making.

How do I get to be a Data Engineer? What training do I need? What are the Data Engineering Tools I need?

Although data engineers are very popular with companies, it’s also a highly technical profession. Training is therefore essential.

This may involve higher education in a school of engineering or computer science. But to increase your chances of entering the job market in the best possible conditions, we advise you to specialize in data engineering. DataScientest makes it possible. Thanks to our Data Engineer training program, you’ll be up and running by the end of the course.


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